Transfer Incremental Learning for Pattern Classification

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Show simple item record Zhu, Zhenfeng en_US Zhu, Xingquan en_US Gua, Yue-Fei en_US Xua, Xiangyang en_US
dc.contributor.editor Xiangji Jimmy Huang, Gareth Jones, Nick Koudas, Xindong Wu, & Kevyn Collins-Thompson en_US 2012-02-02T11:11:43Z 2012-02-02T11:11:43Z 2010 en_US
dc.identifier 2010001767 en_US
dc.identifier.citation Zhu Zhenfeng et al. 2010, 'Transfer Incremental Learning for Pattern Classification', , ACM, USA, , pp. 1709-1712. en_US
dc.identifier.issn 978-1-4503-0099-5 en_US
dc.identifier.other E1 en_US
dc.description.abstract Traditional machine learning methods, such as Support Vector Machines (SVMs), usually assume that training and test data share the same distributions. Due to the inherent dynamic data nature, it is often observed that (1) the volumes of the training data may gradually grow; and (2) the existing and the newly arrived samples may be subject to different distributions or learning tasks. In this paper, we propose a Transfer Incremental Support Vector Machine(TrISVM), with the objective of tackling changes in data volumes and learning tasks at the same time. By using new updating rules to calculate the inverse matrix, TrISVM solves the existing incremental learning problem more efficiently, especially for high dimensional data. Furthermore, when using new samples to update the existing models, TrISVM employs sample-based weight adjustment procedures to ensure that the concept transferring between auxiliary and target samples can be leveraged to fulfill the transfer learning goal. Experimental results on real-world data sets demonstrate that TrISVM achieves better efficiency and prediction accuracy than both incremental-learning and transfer-learning based methods. In addition, the results also show that TrISVM is able to achieve bidirectional knowledge transfer between two similar tasks. en_US
dc.language English en_US
dc.publisher ACM en_US
dc.relation.isbasedon en_US
dc.title Transfer Incremental Learning for Pattern Classification en_US
dc.parent Proceedings of the 19th ACM Conference on Information and Knowledge Management & Co-Located Workshops (CIKM 2010) en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 1709 en_US
dc.identifier.endpage 1712 en_US FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.for 150301 en_US
dc.personcode 0000066566 en_US
dc.personcode 107283 en_US
dc.personcode 0000066567 en_US
dc.personcode 0000066568 en_US
dc.percentage 100 en_US Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence) en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom ACM Conference on Information and Knowledge Managem en_US 20101026 en_US
dc.location.activity Toronto, Ontario, Canada en_US
dc.description.keywords Machine learning, support vector machines, incremental learning, transfer learning en_US
dc.staffid en_US

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